Tone analysis of legal documents

ABSTRACT

A computer-implemented method includes detecting a first set and a second set of citations to a legal case in a plurality of legal documents and a first legal document distinct from the plurality of legal documents, respectively. The computer-implemented method further includes determining tones corresponding to each citation in the first and second sets of citations. The computer-implemented method further includes determining a score for each tone in the first and second sets of tones. The computer-implemented method further includes aggregating a first and subset and a second of the first and second sets of citations, respectively. The computer-implemented method further includes generating an average score for the first and second subsets. The computer-implemented method further includes determining a degree of similarity between the first and second subsets based, in part, on a comparison of average scores. A corresponding computer program product and computer system are also disclosed.

BACKGROUND

The present invention relates generally to the field of sentimentanalysis, and more particularly to analyzing the tone of legaldocuments.

An Opinion of the Court (i.e., “court opinion,” “judicial opinion,”“legal opinion”) is a court's official decision in a case. The opinionis a decision written by one or more justices that details the legalprinciples and rationales that the one or more justices relied upon toreach their decision. The court opinion includes various parts,including the heading information, the prior history, the summary of thefacts, and the opinion (i.e., decision from the court which constitutesthe law). Oftentimes, the court opinion includes multiple opinionsreflecting the different rationales that the justices used to reachtheir decision. If more than half of the justices agree, than a majorityopinion is issued. If some justices agree with the majority opinion, butbase their decision on a different rationale, one or more concurringopinions are issued in addition to the majority opinion. Similarly, ifsome justices disagree with the majority opinion, one or more dissentingopinions are issued.

SUMMARY

A computer-implemented method includes detecting a first set ofcitations to a legal case cited in a plurality of legal documents. Thecomputer-implemented method further includes determining a first set oftones corresponding to each citation in the first set of citations. Thecomputer-implemented method further includes determining a first scorefor each tone in the first set of tones. The computer-implemented methodfurther includes aggregating a first subset of the first set ofcitations, wherein each citation in the first subset shares at least afirst tone. The computer-implemented method further includes generatinga first average score for the first subset. The computer-implementedmethod further includes detecting a second set of citations to the legalcase cited in a first legal document, wherein the first legal documentis not in the plurality of legal documents. The computer-implementedmethod further includes determining a second set of tones correspondingto each citation in the second set of citations. Thecomputer-implemented method further includes determining a second scorefor each tone in the second set of tones. The computer-implementedmethod further includes aggregating a second subset of the second set ofcitations, wherein each citation in the second subset shares at leastthe first tone. The computer-implemented method further includesgenerating a second average score for the second subset. Thecomputer-implemented method further includes determining a first degreeof similarity between the first subset and the second subset based, atleast in part, on a comparison of the first average score and the secondaverage score. A corresponding computer program product and computersystem are also disclosed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of a computing environment,generally designated 100, suitable for operation of a legal citationanalyzer program, in accordance with at least one embodiment of theinvention.

FIG. 2 is a flow chart diagram depicting operational steps for a legalcitation analyzer program, in accordance with at least one embodiment ofthe invention.

FIG. 3 is a functional block diagram of a computing environment,generally designated 300, suitable for operation of a legal citationannotator program, in accordance with at least one embodiment of theinvention.

FIG. 4 is a flow chart diagram depicting operational steps for a legalcitation annotator program, in accordance with at least one embodimentof the invention.

FIG. 5 is a block diagram of a computing apparatus 500 suitable forexecuting a legal citation analyzer program and a legal citationannotator program, respectively, in accordance with embodiments of theinvention.

DETAILED DESCRIPTION

In countries that use a common law system, such as the United States, amajority opinion in a court opinion becomes part of the body of caselaw. Accordingly, the case law can be cited as precedent by later courtswhen issuing their own decisions. Similarly, the case law can be citedas precedent in an attorney's legal brief or memorandum of law to directthe court on how to rule in a case. On the other hand, a dissentingopinion does not create binding precedent nor does it become a part ofthe case law. However, a dissenting opinion can be cited as persuasiveauthority when a justice wants to argue that the court's holding (i.e.,determining of a matter of law based on the issue presented in aparticular case) should be limited or overturned. Similarly, adissenting opinion can be cited in an attorney's legal brief ormemorandum of law in an attempt to persuade the court to limit oroverturn current case law.

The body of case law and dissenting opinions are paramount in anattorney's ability to dictate how a court should rule in a case. Inorder to assist attorneys in their legal research, existingtechnologies, such as LexisNexis® and Westlaw®, hire and train attorneysto analyze the content of court opinions. These “attorney-editors”determine whether a citation to a legal case in a court opinion hasreceived positive, negative, cautionary or neutral treatment byphysically parsing through the court opinion. Embodiments of the presentinvention recognize that hundreds or thousands of court opinions arepublished daily and that physically parsing through each court opinionis both inefficient and time consuming.

Once an attorney-editor determines the treatment of a legal case citedin a court opinion, a “citation signal” or “status flag” can be placednext to the case name to indicate the current treatment of the case. Forexample, a red flag may indicate that the case has negative history(e.g., judicial review was allowed, reconsideration was allowed, thecase was reversed or quashed) or negative treatments (e.g., the case wasnot followed or was questioned by a subsequent court). In anotherexample, a green flag may indicate that the case has positive history(e.g., the case was affirmed, judicial review was denied, or leave toappeal was refused by a higher court) or positive treatments (e.g., thecase is followed or followed in a minority opinion of a subsequentcourt). In yet another example, a yellow flag may indicate that the casehas some negative history or has been distinguished by a subsequentcourt, but has not been reversed or overruled. Similarly, another symbolmay be used to indicate that a case has neutral history (e.g., the caseis abandoned, abated, a leave to appeal was granted, reconsideration wasdenied), or that a case has neutral treatments (e.g., the case wasmentioned, explained, cited, or cited in a dissenting opinion). Of thedifferent symbol indicators that can be assigned to a legal case, amajority of the signals fall under the “cautionary” or “neutral”heading. However, a cautionary or neutral signal is not very useful inthat the reason why a case is associated with a cautionary or neutralsignal is often ambiguous.

Embodiments of the present invention recognize that the process ofassociating a signal with a legal case can be improved by furtherrefining these signals based on mapping a tone to a legal case each timethe case is cited in a court opinion. Embodiments of the presentinvention recognize that the process of associating a signal with alegal case can be improved by breaking down a cited case by emotion,style of language, and social tendencies. Embodiments of the presentinvention recognize that the process of associating a signal with a casecan be improved by providing signals that correspond to an individualjustice's tone regarding a particular legal case. Embodiments of thepresent invention recognize that the process of associating a signalwith a case can be improved by providing signals that correspond to anaverage tone of all of the justices who have cited to a particular legalcase. Various embodiments of the present invention may address orimprove upon some or all of the aforementioned problems ordisadvantages, however it will be understood that addressing anyparticular problem or disadvantage is not a necessary requirement forthe practice of all embodiments of the present invention.

Referring now to various embodiments of the invention in more detail,FIG. 1 is a functional block diagram of a computing environment,generally designated 100, suitable for operation of a legal citationanalyzer (“LCA”) program in accordance with at least one embodiment ofthe invention. FIG. 1 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made by those skilledin the art without departing from the scope of the invention as recitedby the claims.

Computing environment 100 includes computer system 102, user device 103,legal document database 104, and tonal index 105 interconnected overnetwork 106. Network 106 can be, for example, a telecommunicationsnetwork, a local area network (LAN), a wide area network (WAN), such asthe Internet, or a combination of the three, and can include wired,wireless, or fiber optic connections. Network 106 may include one ormore wired and/or wireless networks that are capable of receiving andtransmitting data, voice, and/or video signals, including multimediasignals that include voice, data, and video information. In general,network 106 may be any combination of connections and protocols thatwill support communications between computer system 102, user device103, legal document database 104, tonal index 105, and other computingdevices (not shown) within computing environment 100.

User device 103 can be a laptop computer, tablet computer, smartphone,smartwatch, or any programmable electronic device capable ofcommunicating with various components and devices within computingenvironment 100, via network 106. In general, user device 103 representsany programmable electronic device or combination of programmableelectronic devices capable of executing machine readable programinstructions and communicating with other computing devices (not shown)within computing environment 100 via a network, such as network 106.User device 103 includes user interface 107.

User interface 107 provides an interface between a user of user device103 and computer system 102. In one embodiment, user interface 107 is agraphical user interface (GUI) or a web user interface (WUI) and candisplay text, documents, web browser windows, user options, applicationinterfaces, and instructions for operation, and include the information(such as graphic, text, and sound) that a program presents to a user andthe control sequences the user employs to control the program. Inanother embodiment, user interface 107 is mobile application softwarethat provides an interface between a user of user device 103 andcomputer system 102. Mobile application software, or an “app,” is acomputer program that runs on smartphones, tablet computers,smartwatches and any other mobile devices.

Legal document database 104 includes a plurality of legal documents 108.In some embodiments, plurality of legal documents 108 includes courtopinions and law journal articles. In embodiments of the invention, LCAprogram 101 detects a first set of citations 109 cited to in pluralityof legal documents 108. In embodiments of the invention, LCA program 101determines a first set of tones 110 corresponding to each citation infirst set of citations 109. In embodiments of the invention, LCA program101 aggregates a first subset 111 of first set of citations 109. Eachcitation in first subset 111 shares at least one tone in common. In someembodiments, LCA program 101 aggregates a first portion 112 of firstsubset 111. In some embodiments, each citation in first portion 112 offirst subset 111 corresponds to at least a first justice (i.e., “judge,”“magistrate,” etc.).

In embodiments of the invention, legal document database 104 includes afirst legal document 113. First legal document 113 is distinct fromplurality of legal documents 108. In some embodiments, first legaldocument 113 may be a case brief, memorandum of law, motion, and/or anyother legal document written by a lawyer. In embodiments of theinvention, LCA program 101 detects a second set of citations 114 citedto in first legal document 113. In embodiments of the invention, LCAprogram 101 determines a second set of tones 115 corresponding to eachcitation in second set of citations 114. In embodiments of theinvention, LCA program 101 aggregates a second subset 116 of second setof citations 114. Each citation in second subset 116 shares at least onetone in common. In some embodiments, each citation in first subset 111and each citation in second subset 116 shares at least one tone incommon.

Tonal index 105 stores first set of tones 110 corresponding to eachcitation in first set of citations 109 and second set of tones 115corresponding to each citation in second set of citations 114. Inembodiments of the invention, tonal index 105 stores a first score 117for each tone in first set of tones 110 and a second score 118 for eachtone in second set of tones 115. In embodiments of the invention, tonalindex 105 stores a first average score 119 for first subset 111 and asecond average score 120 for second subset 116. In some embodiments,tonal index 105 stores a third average score 121 for first portion 112of first subset 111. In some embodiments, tonal index 105 is a localindex. In other embodiments, tonal index 105 is a global index.

Computer system 102 can be a standalone computing device, a managementserver, a web server, a mobile computing device, or any other electronicdevice or computing system capable of receiving, sending, and processingdata. In other embodiments, computer system 102 can represent a servercomputing system utilizing multiple computers as a server system, suchas in a cloud computing environment. In an embodiment, computer system102 represents a computing system utilizing clustered computers andcomponents (e.g., database server computers, application servercomputers, etc.) that act as a single pool of seamless resources whenaccessed within computing environment 100. Computer system 102 includesLCA program 101, communicatively coupled to computer system 102.Computer system 102 includes internal and external hardware components,as depicted and described in further detail with respect to FIG. 5.

FIG. 2 is a flow chart diagram depicting operational steps for an LCAprogram in accordance with at least one embodiment of the presentinvention. At step S200, LCA program 101 detects sets of legal citationsto a common legal case cited in legal documents. In embodiments of theinvention, LCA program 101 detects first set of citations 109 to a legalcase (e.g., Jim v. Jones) cited in plurality of legal documents 108. Inembodiments of the invention, LCA program 101 detects second set ofcitations 114 to the same legal case (e.g., Jim v. Jones) cited in firstlegal document 113, wherein first legal document 113 is not in pluralityof legal documents 108. In alternative embodiments of the invention, LCAprogram 101 detects a name of at least a first justice corresponding tofirst set of citations 109. For example, LCA program 101 detects thename of a justice (e.g., Justice Blue) who cited to the legal case Jimv. Jones in a majority opinion, concurring opinion, and/or dissentingopinion of a court opinion.

In some embodiments, LCA program 101 detects sets of citations to acommon legal case and/or the name of at least a first justice throughthe use of natural language processing (“NLP”) software. One example ofNLP software is Watson® Alchemy Language. For example, LCA program 101utilizes NLP software to detect and extract a citation to Jim v. Jonesfrom a PDF file of a court opinion. In another example, LCA program 101utilizes NLP software to detect and extract a citation to the legal caseJim v. Jones from a text file of a lawyer's case brief. In anotherexample, LCA program 101 utilizes NLP software to detect and extract aname of a justice citing to Jim v. Jones in a majority opinion (e.g.,“Blue, Justice, delivered the opinion of the Court”). LCA program 101may repeat step S200 and detect a set of citations for any number ofadditional legal cases cited in plurality of legal documents 108.Similarly, LCA program 101 may repeat step S200 and detect a set ofcitations for any number of additional legal cases cited in first legaldocument 113.

At step S201, LCA program 101 determines a set of tones corresponding toeach set of legal citations. In embodiments of the invention, LCAprogram 101 determines first set of tones 110 corresponding to eachcitation in first set of citations 109. In embodiments of the invention,LCA program 101 determines second set of tones 115 corresponding to eachcitation in second set of citations 114. For example, LCA program 101detects citations to the legal cases Adams (Adams, 82 New Delaware St.7dat 259, 357 N.E.3d at 79) and Burns (Burns, 54 New Island St. 3d at 349,573 N.E.5d at 43) cited in the following portion of an Opinion of theCourt written in the majority decision in the case Patent v.Infringement:

-   -   In spite of these observations, however, a majority of this        court expressly eschewed the Adams approach in Burns. Though the        New Delaware Court determined that still another safeguard for        infringement of computer software was unnecessary, Adams, 82 New        Delaware St.7d at 259, 357 N.E.3d at 79, this court decided that        the New Delaware Constitution requires a different analytical        focus—a categorical determination of whether, under the totality        of the circumstances, a defendant may be liable for inducing        infringement of a patent under 53 U.S.C. § 123(a) when no one        has directly infringed the patent under § 123(b) or any other        statutory provision. Burns, 54 New Island St. 3d at 349, 573        N.E.5d at 43.        Based on the text prior to and/or subsequent to the citation to        Adams, LCA program 101 determines a tone of “opposition.”        Similarly, based on the text prior to and/or subsequent to the        citation to Burns, LCA program 101 determines a tone of        “approval.”

In some embodiments, LCA program 101 determines one or more tones infirst set of tones 110 and second set of tones 115 from a predefinedcorpus of sentimental tones. For example, LCA program 101 determinestones of frustration, outrage, protest, and piqued for the sentiment“anger.” In another example, LCA program 101 determines tones ofdisappointment and disapproval for the sentiment “sadness.” In anotherexample, LCA program 101 determines tones of approval, compassion,support, and esteem for the sentiment “joy.” In another example, LCAprogram 101 determines tones of dislike, objection, opposition,rejection, dissatisfaction, and dispute for the sentiment “disgust.” Inanother example, LCA program 101 determines tones of concern, unease,doubt, and dismay for the sentiment “fear.”

In some embodiments, LCA program 101 determines one or more tones from apredefined corpus of tonal languages. For example, LCA program 101determines liberal, moderate, and conservative political connotationsand/or rhetorical connotations. LCA program 101 may repeat step S201 anddetermine a set of tones corresponding to a set of citations for anynumber of additional legal cases cited in plurality of legal documents108. Similarly, LCA program 101 may repeat step S201 and determine a setof tones corresponding to a set of citations for any number ofadditional legal cases cited in first legal document 113.

In embodiments of the invention, LCA program 101 determines one or moretones in first set of tones 110 and second set of tones 115 through theuse of sentiment analysis (i.e., opinion mining) software. Sentimentanalysis aims to determine the attitude of an individual or group withrespect to a particular topic. More specifically, sentiment analysisrefers to the use of NLP software, text analysis software, andcomputational linguistics software to identify and extract subjectiveinformation in source materials. For example, attitude may be associatedwith a justice's judgment, reasoning, or evaluation with respect to acitation to the legal case Jim v. Jones in a court opinion. In anotherexample, attitude may be associated with a justice's affective state(i.e., emotional state or tone) with respect to a citation to the legalcase Jim v. Jones in a court opinion. In another example, attitude maybe associated with a justice's style of language, political principles(e.g., liberal, moderate, and conservative), or rhetorical connotations(e.g., liberal, neutral, and conservative) with respect to a citation tothe legal case Jim v. Jones in a court opinion.

In some embodiments, LCA program 101 determines one or more tones infirst set of tones 117 and second set of tones 118 through the use of ananalytics engines. One example of an analytics engine is IBM® WatsonTone Analyzer. For example, LCA program 101 detects a citation to Jim v.Jones in a court opinion. Based on the text prior to and/or subsequentto the citation to Jim v. Jones, LCA program 101 utilizes an analyticsengine to determine tones of “rejection” and “dissatisfaction.” In someembodiments, LCA program 101 employs NLP and text analysis (i.e.,deriving high-quality information from text through the devising ofpatterns and trends through means such as statistical pattern learning)to determine subjective information about first set of citations 109 andsecond set of citations 114. For example, LCA program 101 receives acase brief and identifies a citation to the legal case Jim v. Jones.Based on text prior to and/or subsequent to the citation to Jim v.Jones, LCA program 101 utilizes NLP and text analysis to determine tonesof “approval” and “esteem.”

At step S202, LCA program 101 determines a score for each tone in eachset of tones. In embodiments of the invention, LCA program 101determines first score 117 for each tone in first set of tones 110 andsecond score 118 for each tone in second set of tones 115. In someembodiments, first score 117 and second score 118 are determined based,at least in part, on a level of intensity of each tone corresponding toa citation. In some embodiments, a level of intensity is determined bythe words and/or the context of words prior to and after a citation. Insome embodiments, LCA program 101 determines first score 117 and secondscore 118 for each tone of a corpus of tones, regardless of whether ornot a tone corresponds to a citation. For example, a corpus of tones mayinclude the following tones: “frustration,” “protest,” “disapproval,”“approval,” “support,” and “doubt.” LCA program 101 may determine tonesof “frustration” and “objection” with respect to a citation to the legalcase Jim v. Jones in a court opinion. Here, LCA program 101 may assignscores of 0.8 for the tone “frustration” and 0.3 for the tone“objection” based on the words and/or the context of words prior to andafter the citation to Jim v. Jones. Similarly, LCA program 101 mayassign a score of 0.0 for the remaining tones of the corpus of tonesthat do not correspond to a citation to the legal case Jim v. Jones inthe court opinion. In an embodiment, each score is a numerical fraction(e.g., 0.0 to 1.0). In an embodiment, each score is a numerical wholenumber (e.g., one through ten). In an embodiment, each score is a range(e.g., low, medium, or high). In other embodiments, the scores may berepresented by any generally known means of ranking each tone in firstset of tones 110 and second set of tones 115.

At step S203, LCA program 101 aggregates one or more subsets for eachset of legal citations. Each citation in a subset of a set of citationsshares at least a first common tone (e.g., “frustration”). In someembodiments, each citation in a subset of a set of citations shares atleast two or more common tones (e.g., “frustration” and “rejection”). Inembodiments of the invention, LCA program 101 aggregates first subset111 of first set of citations 109 and second subset 116 of second set ofcitations 114. For example, LCA program 101 detects 500 citations to Jimv. Jones in first set of citations 109. Accordingly, LCA program 101determines first set of tones 110 corresponding to each of the 500citations to Jim v. Jones in first set of citations 109. In thisexample, assume that first set of tones 110 includes 200 citationscorresponding to the tone “frustration,” 15 citations corresponding tothe tone “approval,” 20 citations corresponding to the tone “support,”150 citations corresponding to the tone “rejection,” and 115 citationscorresponding to the tone “concerned.” Here, LCA program 101 aggregatesfirst subset 111 of first set of citations 109, wherein each citation toJim v. Jones in first subset 111 shares at least a first common tone(e.g., “frustration”). LCA program 101 may aggregate additional subsetsof sets of citations for each type of tone corresponding to a set ofcitations.

In some embodiments, LCA program 101 aggregates first portion 112 offirst subset 111. First portion 112 includes a portion of citationscited to by at least a first justice and sharing at least a first commontone. Continuing with the previous example, assume that LCA program 101detected that Justice Blue cited to Jim v. Jones in 15 citations of the200 citations of first subset 111 of first set of citations 109.Accordingly, first portion 112 of first subset 111 includes 15 citationsto Jim v. Jones by Justice Blue, wherein each citation in first portion112 shares at least the tone “frustration.”

At step S204, LCA program 101 generates an average score for each subsetof each set of legal citations. In embodiments of the invention, LCAprogram 101 generates first average score 119 for first subset 111 offirst set of citations 109 and second average score 120 for secondsubset 116 of second set of citations 114. In the previous example, LCAprogram 101 aggregated first subset 111 of first set of citations 109 toJim v. Jones, wherein first subset 111 included 200 citationscorresponding to the tone “frustration.” Based on first score 117determined for each citation corresponding to the tone “frustration” infirst subset 111, LCA program 101 may generate first average score 119(e.g., 0.7). Similarly, LCA program 101 may generate an average scorefor any additional subsets of a set of citations, wherein eachadditional subset shares a common tone of “approval,” “support,”“rejection,” and “concerned,” respectively. Based on each additionalsubset, LCA program 101 may generate first average score 119 (e.g., 0.3)for the tone “approval,” first average score 119 (e.g., 0.1) for thetone “support,” first average score 119 (e.g., 0.9) for the tone“rejection,” and first average score 119 (e.g., 0.5) for the tone“concerned.” In some embodiments, LCA program 101 generates an averagescore of 0 for any tones in a corpus of tones that are not representedin a subset.

In some embodiments, LCA program 101 generates third average score 121for first portion 112 of first subset 111 of first set of citations 109.Continuing with the previous example, first portion 112 of first subset111 included 15 citations to Jim v. Jones by Justice Blue, wherein eachcitation in first portion 112 shares at least the tone “frustration.”Based on second score 118 determined for each citation corresponding tothe tone “frustration” in first portion 112, LCA program 101 maygenerate third average score 121 (e.g., 0.9). LCA program 101 maygenerate an average score for a portion of any additional subsets of aset of citations. In some embodiments, LCA program 101 generates thirdaverage score 121 of 0 for any tones in a corpus of tones that are notrepresented in a first portion of a first subset.

At step S205, LCA program 101 determines a degree of similarity betweentwo subsets based, at least in part, on a comparison of average scores.The degree of similarity between two subsets is further based on eachsubset sharing at least a first common tone. In embodiments of theinvention, LCA program 101 determines a degree of similarity betweenfirst average score 119 for first subset 111 of first set of citations109 and second average score 120 for second subset 116 of second set ofcitations 114. In some embodiments, LCA program 101 determines a degreeof similarity between second average score 120 for second subset 116 ofsecond set of citations 114 and third average score 121 for firstportion 112 of first subset 111 of first set of citations 109. Forexample, LCA program 101 may generate first average score 119 of 0.9 forfirst subset 111 of first set of citations 109, wherein each citation infirst subset 111 corresponds to the tone “rejection.” Similarly, LCAprogram 101 may generate second average score 120 of 0.4 for secondsubset 116 of second set of citations 114, wherein each citation insecond subset 116 also corresponds to the tone “rejection.” Here, LCAprogram 101 determines a degree of similarity of 44 percent (0.4/0.9).In some embodiments, LCA program 101 flags a citation in first legaldocument 113 if the degree of similarity is below a given threshold(e.g., 75 percent). In some embodiments, LCA program 101 flags acitation in first legal document 113 is the degree of similarity isabove a given threshold (e.g., 90 percent).

In some embodiments, the comparison of the average scores is based oncalculating a standard deviation of scores. For example, LCA program 101generates first average score 119 of 0.7 for first subset 111 (e.g.,tone of “support”) corresponding to first set of citations 109 (e.g.,Jim v. Jones) cited in a plurality of court opinions. Similarly, LCAprogram 101 generates second average score 120 of 0.6 for second subset116 (e.g., tone of “support”) corresponding to second set of citations114 (e.g., Jim v. Jones) cited in a lawyer's case brief. Based on acalculated standard deviation of scores, LCA program 101 determineswhether the average strength of the lawyer's tone of “support” (i.e.,average score of 0.6) corresponding to the citation to Jim v. Jones inthe case brief is similar (i.e., within one standard deviation) to theaverage strength of the tone of “support” (i.e., average score of 0.7)in the plurality of court opinions. In some embodiments, LCA program 101flags a citation in first legal document 113 if the degree of similarityis above a given threshold (e.g., more than one standard deviation).

In some embodiments, LCA program 101 generates at least a firstsuggested legal document (e.g., a court opinion) in first plurality oflegal documents 108. In some embodiments, the at least first suggestedlegal document is generated based on identifying a tone corresponding toa citation in first subset 111 and identifying the same tonecorresponding to the same citation in second subset 116. In someembodiments, the at least first suggested legal document is generatedbased on identifying a tone having a score that corresponds to acitation in first subset 111 and identifying the same score for the sametone that corresponds to same citation in second subset. For example,LCA program 101 suggests a first suggested legal document in firstplurality of legal documents 108 based on identifying a citation to Jimv. Jones cited in the second legal document (e.g., court opinion) thathas a tone (e.g., tone of “support”) whose first score 119 (e.g., 0.8)matches the second score 120 (e.g., 0.8) of the tone of “support”corresponding to the citation to Jim v. Jones cited in the first legaldocument (e.g., a lawyer's memorandum of law). In other words, LCAprogram 101 suggests a legal document (e.g., court opinion) thatcontains a citation to a legal case whose tone and score matches thetone and score of the words prior and/or or subsequent to the same legalcase cited in a lawyer's legal document (e.g., case brief).

In some embodiments, LCA program 101 generates at least a firstsuggested legal document if a citation in first legal document isflagged. In some embodiments, LCA program 101 generates at least a firstsuggested legal document if a degree of similarity between first averagescore 119 for first subset 111 of first set of citations 109 and secondaverage score 120 for second subset 116 is below a given threshold. Insome embodiments, LCA program 101 generates at least a first suggestedlegal document if a degree of similarity between first average score 119for first subset 111 of first set of citations 109 and second averagescore 120 for second subset 116 is above a given threshold. In someembodiments, LCA program 101 automatically replaces a citation to alegal case cited in first legal document with a citation to a legal casecited in a first suggested legal document (e.g., court opinion) inplurality of legal documents 108.

In some embodiments, LCA program 101 generates at least a secondsuggested legal document (e.g., a court opinion) in first plurality oflegal documents 108. In some embodiments, the at least second suggestedlegal document is generated based on identifying a tone corresponding toa citation in second subset 116 and identifying the same tonecorresponding to the same citation in first portion 112 of first subset111. In some embodiments, the at least first suggested legal document isgenerated based on identifying a tone having a score that corresponds toa citation in first subset 111 and identifying the same score for thesame tone that corresponds to same citation in second subset. Forexample, LCA program 101 suggests a second suggested legal documentbased identifying Justice Brown's citation to Jim v. Jones in a legaldocument (e.g., court opinion) having a corresponding tone of“rejection” whose first score 119 (e.g., 0.7) matches the second score120 (e.g., 0.7) of the tone of “rejection” corresponding to the citationto Jim v. Jones cited in a lawyer's case brief. In other words, LCAprogram 101 may identify a legal document (e.g., court opinion) thatcontains a citation to a legal case by a particular justice whose toneand score matches the tone and score of the words prior to and/or orsubsequent to the same legal case cited in a lawyer's legal document(e.g., memorandum of law).

FIG. 3 is a functional block diagram of a computing environment,generally designated 300, suitable for operation of a legal citationannotation (“LCA”) program, in accordance with at least one embodimentof the invention. FIG. 3 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made by those skilledin the art without departing from the scope of the invention as recitedby the claims.

Computing environment 300 includes computer system 102, user device 103,legal document database 304, and tonal index 305 interconnected overnetwork 106. Computer system 102 includes LCA program 301,communicatively coupled to computer system 102. Computer system 102includes internal and external hardware components, as depicted anddescribed in further detail with respect to FIG. 5.

Legal document database 304 includes a plurality of legal documents 308.In some embodiments, plurality of legal documents 308 includes courtopinions. In some embodiments, plurality of legal documents 308 includeslaw journal articles. In embodiments of the invention, LCA program 301detects a first set of citations 309 cited to in plurality of legaldocuments 308. In embodiments of the invention, LCA program 301determines a first set of tones 310 corresponding to each citation infirst set of citations 309. In embodiments of the invention, LCA program301 detects a first set of annotations 311 corresponding to eachcitation in first set of citations 309. In embodiments of the invention,LCA program 301 aggregates a first subset 312 of first set ofannotations 311. Each annotation in first subset 312 shares a commonannotation. In embodiments of the invention, an annotation may be anygenerally known means of indicating or displaying a case treatmentcorresponding to a legal citation.

In embodiments of the invention, legal document database 304 includesfirst legal document 313. First legal document 313 is distinct fromplurality of legal documents 308. In some embodiments, first legaldocument 313 is a court opinion. In some embodiments, first legaldocument 313 is a law journal article. In embodiments of the invention,LCA program 301 detects a second set of citations 314 cited to in firstlegal document 313. In embodiments of the invention, LCA program 301determines a second set of tones 315 corresponding to each citation insecond set of citations 314. In embodiments of the invention, LCAprogram 301 generates second set of annotations 316 based on second setof tones 315 corresponding to each citation in second set of citations314. In embodiments of the invention, second set of annotations 316includes a plurality of annotations corresponding to each citation insecond set of citations 314 cited in first legal document 313.

Tonal index 305 stores first set of tones 310 corresponding to eachcitation in first set of citations 309 and second set of tones 315corresponding to each citation in second set of citations 314. Inembodiments of the invention, tonal index 305 stores a first score 317for each tone in first set of tones 310 and a second score 318 for eachtone in second set of tones 315. In some embodiments, tonal index 305 isa local index. In other embodiments, tonal index 305 is a global index.

FIG. 4 is a flow chart diagram depicting operational steps for a legalcitation annotator (“LCA”) program 301 in accordance with at least oneembodiment of the present invention. At step S400, LCA program 301detects sets of legal citations to a plurality of legal cases cited inlegal documents. In embodiments of the invention, LCA program 301detects first set of citations 309 cited in plurality of legal documents308. In embodiments of the invention, LCA program 301 detects second setof citations 314 cited in first legal document 313, wherein first legaldocument 313 is not part of plurality of legal documents 308. Inembodiments of the invention, plurality of legal documents 308 areannotated court opinions and/or law review articles. In someembodiments, first legal document 313 is a court opinion that has yet tobe annotated (i.e., first legal document 313 is devoid of any casetreatments). In some embodiments, first legal document 313 is a lawreview article that has yet to be annotated. In some embodiments, LCAprogram 301 detects sets of citations through the use of NLP software.In some embodiments, LCA program 301 detects sets of citations throughthe use of Watson® Alchemy Language.

At step 401, LCA program 301 determines a set of tones corresponding toeach set of legal citations. In embodiments of the invention, LCAprogram 301 determines sets of tones in accordance with the methodsemployed by LCA program 101. In embodiments of the invention, LCAprogram 301 determines first set of tones 310 corresponding to eachcitation in first set of citations 309. In embodiments of the invention,LCA program 301 determines second set of tones 315 corresponding to eachcitation in second set of citations 314. In some embodiments, LCAprogram 301 determines one or more tones in first set of tones 310 andsecond set of tones 315 from a predefined corpus of sentimental tones.For example, LCA program 301 may determine tones of frustration,outrage, protest, and piqued for the sentiment “anger.” In anotherexample, LCA program 301 may determine tones of disappointment anddisapproval for the sentiment “sadness.” In another example, LCA program301 may determine tones of approval, compassion, support, and esteem forthe sentiment “joy.” In another example, LCA program 301 may determinetones of dislike, objection, opposition, rejection, dissatisfaction, anddispute for the sentiment “disgust.” In another example, LCA program 301may determine tones of concern, unease, doubt, and dismay for thesentiment “fear.”

In embodiments of the invention, LCA program 301 determines one or moretones in first set of tones 310 and second set of tones 315 through theuse of sentiment analysis (i.e., opinion mining) software (e.g., NLPsoftware, text analysis software, and computational linguistics). Insome embodiments, LCA program 301 determines one or more tones in firstset of tones 310 and second set of tones 315 through the use of ananalytics engines, such as IBM® Watson Tone Analyzer. In someembodiments, LCA program 301 employs NLP and text analysis (i.e.,deriving high-quality information from text through the devising ofpatterns and trends through means such as statistical pattern learning)to determine subjective information about first set of citations 309 andsecond set of citations 314.

At step S402, LCA program 301 determines a score for each tone in eachset of tones. In embodiments of the invention, LCA program 301determines first score 317 for each tone in first set of tones 310 andsecond score 318 for each tone in second set of tones 315. In someembodiments, first score 317 and second score 318 are determined based,at least in part, on a level of intensity of each tone corresponding toa citation. In some embodiments, a level of intensity is determined bythe words and/or the context of words prior to and after a citation. Insome embodiments, LCA program 301 determines first score 317 and secondscore 318 for each tone of a corpus of tones, regardless of whether ornot a tone corresponds to a citation. For example, a corpus of tones mayinclude the following tones: “frustration,” “protest,” “disapproval,”“approval,” “support,” and “doubt.” LCA program 301 may determine tonesof “frustration” and “objection” with respect to a citation to the legalcase Howard v. Hughes in a court opinion. Here, LCA program 301 mayassign scores of 0.3 for the tone “frustration” and 0.6 for the tone“objection” based on the words and/or the context of words prior to andafter the citation to Howard v. Hughes. Similarly, LCA program 101 mayassign a score of 0.0 for the remaining tones of the corpus of tonesthat do not correspond to a citation to the legal case Howard v. Hughesin the court opinion. In an embodiment, each score is a numericalfraction (e.g., 0.0 to 1.0). In an embodiment, each score is a numericalwhole number (e.g., one through ten). In an embodiment, each score is arange (e.g., low, medium, or high). In other embodiments, the scores maybe represented by any generally known means of ranking each tone infirst set of tones 317 and second set of tones 318.

At step S403, LCA program 301 detects a set of annotations correspondingto first set of citations 309. In embodiments of the invention, LCAprogram 301 detects first set of annotations 311 corresponding to firstset of citations 309 in plurality of legal documents 308. In embodimentsof the invention, a set of annotations are case treatments associatedwith citations to legal cases cited in a plurality of court opinions.Here, each citation of first set of citations 309 cited to in pluralityof legal documents 308 has already been previously annotated with arespective case treatment. A case treatment is a means of denoting thetype of judicial treatment a legal case has received when cited in courtopinions. For example, the case treatment “distinguished” indicates thata legal case is held to be inapplicable because of a difference in factor law. In another example, the case treatment “questioned” indicatesthat a citing case has criticized the conclusion or reasoning of a citedcase, but has not directly refused to follow it. In yet another example,the case treatment “followed” indicates that a citing case has, in amajority or plurality opinion, applied a principle of law from the citedcase.

In some embodiments, LCA program 301 detects an annotation through theuse of Watson® Alchemy Language. In some embodiments, LCA program 301detects an annotation through the use of text extraction software, suchas NLP. In some embodiments, LCA program 301 detects an annotationthrough the use of approximate string matching methods (i.e., fuzzystring searching), such as edit distance. Here, the closeness of a matchbetween a text string and a pattern is measured in terms of the numberof primitive operations necessary to convert the string into an exactmatch. For example, LCA program 301 matches the word “limits” to theword “limited” to identify an annotation (e.g., the case treatment“limited”). In some embodiments, LCA program 301 detects an annotationby comparing words surrounding a citation to a legal case to a pluralityof known case treatments. For example, the words are compared literally,semantically, and/or conceptually to the plurality of known casetreatments. In embodiments of the invention, LCA program 301 uses aconceptual dictionary, such as WordNet®, to determine whether theconcept underlying the words matches the concept underlying one or moreof the plurality of known case treatments. In some embodiments, LCAprogram 301 detects an annotation corresponding to a citation to a legalcase through the use of Watson® Alchemy Language.

At step S404, LCA program 301 aggregates one or more subsets for firstset of annotations 311 corresponding to first set of citations 309. Eachannotation in a subset of a set of annotations shares at least a firstcommon annotation (e.g., the case treatment “followed”). In someembodiments, each annotation in a subset of a set of annotations sharesat least two or more common annotations (e.g., the case treatments“followed” and “explained”). In embodiments of the invention, LCAprogram 301 aggregates first subset 312 of first set of annotations 311.For example, LCA program 301 detects 1,000 annotations corresponding to1,000 citations of first set of citations 309 from plurality of legaldocuments 308. In this example, assume that first set of annotations 311includes 300 annotations corresponding to the case treatment“distinguished,” 200 annotations corresponding to the case treatment“followed,” 100 annotations corresponding to the case treatment“questioned,” 200 annotations corresponding to the case treatment“explained,” 100 annotations corresponding to the case treatment “notfollowed,” and 100 annotations corresponding to the case treatment“limited.” Here, LCA program 301 aggregates first subset 312 of firstset of annotations 311, wherein each annotation in first subset 312shares at least a first common annotation (e.g., first subset 312includes 300 annotations corresponding to the case treatment“distinguished”). LCA program 101 may aggregate additional subsets ofsets of annotations for each type of annotation (i.e., case treatment)in first set of annotations 311.

At step S405, LCA program 301 builds a training model, wherein thetraining model is built based, at least in part, from first subset 312of first set of annotations 311 corresponding to first set of citations309. In some embodiments, LCA program 301 builds the training modelbased on multiple subsets derived from multiple sets of annotationscorresponding to multiple sets of citations. In some embodiments, LCAprogram 301 builds the training model through the use of machinelearning. In machine learning, support vector machines (SVMs) areapplied to analyze data and recognize patterns. An SVM is a form ofcomputer software that includes supervised learning, wherein supervisedlearning is the machine learning task of inferring a function fromlabeled training data (i.e., training samples). In embodiments of theinvention, LCA program 301 may use an SVM solver or tool, such as alibrary for support vector machines (“LIBSVM”). For example, LCA program301 builds a training model based on labeled training data (e.g., knownannotations corresponding to citations in legal documents). In someembodiments, LCA program 301 builds a training model with first subset312 of first set of annotations 311 corresponding to first set ofcitations 309. In some embodiments, LCA program 301 builds a trainingmodel with multiple subsets derived from multiple sets of annotationscorresponding to multiple sets of citations. Accordingly, LCA program301 utilizes SVM software to infer, based on the training model, a casetreatment associated with a citation that has yet to be annotated with acase treatment.

In some embodiments, LCA program 301 builds the training model from acorpus of legal documents annotated with known case treatments. In someembodiments, LCA program 301 builds the training model by mapping eachannotation in first subset 312 of first set of annotations 309 to one ormore tones of first set of tones 310. For example, LCA program 301determines tones of “frustration” and “disgust” with respect to acitation to the legal case Howard v. Hughes cited in a court opinion.Based on an annotation (e.g., the case treatment “disagreed”)corresponding to the citation to Howard v. Hughes cited in the courtopinion, LCA program 301 trains the model by mapping tones of“frustration” and “disgust” to the case treatment “disagreed.”Similarly, LCA program 301 may determine tones of “frustration” and“dissatisfaction” with respect to a citation to the legal case John v.Jackson cited in a court opinion. Based on an annotation (e.g., the casetreatment “disagreed”) corresponding to the citation to John v. Jacksoncited in the court opinion, LCA program 301 trains the model by mappingtones of “frustration” and “dissatisfaction” to the case treatment“disagreed.” LCA program 301 may repeat step S405 for each annotation infirst subset 312, as well as each additional subset aggregated fromfirst set of annotations 309. Similarly, LCA program 301 may repeat stepS405 for each subset aggregated from any additional sets of annotations.

In some embodiments, LCA program 301 builds the training model bymapping each annotation in first subset 312 of first set of annotations309 to each first score 317 corresponding to each tone of first set oftones 310. In the previous example, LCA program 301 determined tones of“frustration” and “disgust” with respect to the citation to the legalcase Howard v. Hughes cited in a court opinion. In this example, LCAprogram may further determine first score 317 of 0.7 for the tone of“frustration” and first score 317 of 0.9 for the tone of “disgust”corresponding to the citation to Howard v. Hughes cited in the courtopinion. Based on an annotation (e.g., the case treatment “disagreed”)associated with the citation to Howard v. Hughes cited in the courtopinion, LCA program 301 trains the model by mapping the tone of“frustration” (first score 317 of 0.7) and the tone of “disgust” (firstscore 317 of 0.9) to the case treatment “disagreed”. Similarly, LCAprogram 301 may determine first score 317 of 0.8 for the tone of“frustration” and first score 317 of 0.6 for the tone of“dissatisfaction” corresponding to the citation to John v. Jackson citedin the court opinion. Based on an annotation (e.g., the case treatment“disagreed”) associated with the citation to John v. Jackson cited inthe court opinion, LCA program 301 trains the model by mapping the toneof “frustration” (first score 317 of 0.8) and the tone of“dissatisfaction” (first score 317 of 0.6) to the case treatment“disagreed.”

At step S406, LCA program 301 determines second set of annotations 316corresponding to second set of citations 314 in first legal document313. Each citation of second set of citations 314 cited in first legaldocument 313 has yet to be annotated with a respective case treatmentand corresponds to a respective case treatment. In some embodiments, LCAprogram 301 determines each annotation of second set of annotations 316based, at least in part, on analyzing the training model and mapping,according to the training model, at least one tone corresponding to eachcitation in second set of citations to a case treatment. For example,LCA program 301 detects a citation to the legal case Mark v. Michaelscited in a court opinion. Here, the citation to Mark v. Michaels citedin the court opinion is devoid of any associated annotations. In thisexample, assume that LCA program 301 also determines tones of“frustration” and “disgust” corresponding to the citation to Mark v.Michaels cited in the court opinion. Based on determining tones of“frustration” and “disgust,” LCA program 301 analyzes the training modelto map the tones “frustration” and “disgust” to the case treatment“criticized.”

In some embodiments, LCA program 301 further determines each annotationof second set of annotations 316 based on analyzing the training modeland mapping, based on the training model, at least one second score 318of at least one tone corresponding to each citation in second set ofcitations 314 to a case treatment. Continuing with the previous example,LCA program 301 determines second score 318 of 0.8 for the tone of“frustration” and second score 318 of 0.7 for the tone of “disgust.”Here, LCA program 301 analyzes the training model to map second scoresof 0.8 and 0.7 for the tones “frustration” and “anger,” respectively, tothe case treatment “criticized.” In some embodiments, LCA program 301determines that a citation is associated with a case treatment if one ormore tones corresponding to the citation have a score above a giventhreshold (e.g., the case treatment “criticized” is associated to acitation if scores corresponding to the tones of “anger” and “disgust”exceed 0.7 and 0.6, respectively). LCA program 301 may repeat step S406for each citation in second set of citations of first legal document.

At step S407, LCA program 301 annotates each citation in second set ofcitations 314 of first legal document 313 with a correspondingannotation from second set of annotations 316 determined in step S406.In an embodiment, LCA program 301 annotates a citation through the useof a footnote interface that displays information about the casetreatment below the text. In an embodiment, LCA program 301 annotates acitation through the use of an aligned annotation that displaysinformation about the case treatment vertically in the text margins. Inan embodiment, LCA program 301 annotates a citation through aninterlinear citation that attaches the annotation directly into a text.In an embodiment, LCA program 301 annotates a citation through the useof a hover box (i.e., a graphical control element that is activated whena user moves or “hovers” a mouse pointer over its trigger area).

FIG. 5 is a block diagram depicting components of a computer 500suitable for executing LCA program 101 and LCA program 301. FIG. 5displays the computer 500, the one or more processor(s) 504 (includingone or more computer processors), the communications fabric 502, thememory 506, the RAM 516, the cache 518, the persistent storage 508, thecommunications unit 512, the I/O interfaces 514, the display 522, andthe external devices 520. It should be appreciated that FIG. 5 providesonly an illustration of one embodiment and does not imply anylimitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironment may be made.

As depicted, the computer 500 operates over a communications fabric 502,which provides communications between the computer processor(s) 504,memory 506, persistent storage 508, communications unit 512, andinput/output (I/O) interface(s) 514. The communications fabric 502 maybe implemented with any architecture suitable for passing data orcontrol information between the processors 504 (e.g., microprocessors,communications processors, and network processors), the memory 506, theexternal devices 520, and any other hardware components within a system.For example, the communications fabric 502 may be implemented with oneor more buses.

The memory 506 and persistent storage 508 are computer readable storagemedia. In the depicted embodiment, the memory 506 comprises a randomaccess memory (RAM) and a cache 518. In general, the memory 506 maycomprise any suitable volatile or non-volatile one or more computerreadable storage media.

Program instructions for LCA program 101 and LCA program 301 may bestored in the persistent storage 508, or more generally, any computerreadable storage media, for execution by one or more of the respectivecomputer processors 504 via one or more memories of the memory 506. Thepersistent storage 508 may be a magnetic hard disk drive, a solid statedisk drive, a semiconductor storage device, read-only memory (ROM),electronically erasable programmable read-only memory (EEPROM), flashmemory, or any other computer readable storage media that is capable ofstoring program instructions or digital information.

The media used by the persistent storage 508 may also be removable. Forexample, a removable hard drive may be used for persistent storage 508.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of the persistentstorage 508.

The communications unit 512, in these examples, provides forcommunications with other data processing systems or devices. In theseexamples, the communications unit 512 may comprise one or more networkinterface cards. The communications unit 512 may provide communicationsthrough the use of either or both physical and wireless communicationslinks. In the context of some embodiments of the present invention, thesource of the source of the various input data may be physically remoteto the computer 500 such that the input data may be received and theoutput similarly transmitted via the communications unit 512.

The I/O interface(s) 514 allow for input and output of data with otherdevices that may operate in conjunction with the computer 500. Forexample, the I/O interface 514 may provide a connection to the externaldevices 520, which may be as a keyboard, keypad, a touch screen, orother suitable input devices. External devices 520 may also includeportable computer readable storage media, for example thumb drives,portable optical or magnetic disks, and memory cards. Software and dataused to practice embodiments of the present invention may be stored onsuch portable computer readable storage media and may be loaded onto thepersistent storage 508 via the I/O interface(s) 514. The I/Ointerface(s) 514 may similarly connect to a display 522. The display 522provides a mechanism to display data to a user and may be, for example,a computer monitor.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a readable storage medium that can direct acomputer, a programmable data processing apparatus, and/or other devicesto function in a particular manner, such that the computer readablestorage medium having instructions stored therein comprises an articleof manufacture including instructions which implement aspects of thefunction/act specified in the flowchart and/or block diagram block orblocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof computer program instructions, which comprises one or more executableinstructions for implementing the specified logical function(s). In somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method, comprising:determining a first set of sentiment attributes corresponding to a firstset of citations to a case cited in a plurality of text files based, atleast in part, on (i) natural language processing and (ii) sentimentanalysis; generating a first score for each type of sentiment attributeassociated with a citation to the case in the first set of citations;determining a second set of sentiment attributes corresponding to acitation to the case cited in a first text file outside of the pluralityof text files based, at least in part, on (i) natural languageprocessing and (ii) sentiment analysis; generating a second score foreach type of sentiment attribute associated with the citation to thecase cited in the first text file; determining that a first degree ofsimilarity between a first average score of a first type of sentimentattribute associated with the first set of citations to the case citedin the plurality of text files and the second score of the first type ofsentiment attribute associated with the citation to the case cited inthe first text file is below a first predetermined threshold;generating, in response to the determination that the first degree ofsimilarity is below the first predetermined threshold, a search querybased on the second score of the first type of sentiment attributecorresponding to the citation to the case cited in the first text file;and processing the search query to return a second text file from theplurality of text files that includes a citation to the case, whereinthe citation to the case included in the second text file matches thesecond score of the first type of sentiment attribute corresponding tothe citation to the case cited in the first text file.
 2. Thecomputer-implemented method of claim 1, wherein the first score and thesecond score are determined based, at least in part, on a level ofintensity of a sentiment attribute corresponding to a citation to thecase.
 3. The computer-implemented method of claim 1, further comprisingflagging the citation in the first text file if the first degree ofsimilarity between the first average score of the first type ofsentiment attribute associated with the first set of citations to thecase cited in the plurality of text files and the second score of thefirst type of sentiment attribute associated with the citation to thecase cited in the first text file is below the first predeterminedthreshold.
 4. The computer-implemented method of claim 1, furthercomprising: aggregating a first portion of citations included in thefirst set of citations, wherein: each citation in the first portion ofcitations corresponds to at least a first entity; and each citation inthe first portion of citations includes the first type of sentimentattribute.
 5. The computer-implemented method of claim 4, furthercomprising: determining that a second degree of similarity between thesecond score of the first type of sentiment attribute associated withthe citation to the case cited in the first text file and a secondaverage score of the first type of sentiment attribute associated withthe first portion of citations to the case cited in the plurality oftext files is below a predetermined threshold.
 6. A computer programproduct, the computer program product comprising one or morenon-transitory computer readable storage media and program instructionsstored on said one or more non-transitory computer readable storagemedia, said program instructions comprising instructions to: determine afirst set of sentiment attributes corresponding to a first set ofcitations to a case cited in a plurality of text files based, at leastin part, on (i) natural language processing and (ii) sentiment analysis;generate a first score for each type of sentiment attribute associatedwith a citation to the case in the first set of citations; determine asecond set of sentiment attributes corresponding to a citation to thecase cited in a first text file that is outside of the plurality of textfiles based, at least in part, on (i) natural language processing and(ii) sentiment analysis; generate a second score for each type ofsentiment attribute associated with the citation to the case in thefirst text file; determine that a first degree of similarity between afirst average score of a first type of sentiment attribute associatedwith the first set of citations to the case cited in the plurality oftext files and the second score of the first type of sentiment attributeassociated with the citation to the case cited in the first text file isbelow a first predetermined threshold; generate, in response to thedetermination that the first degree of similarity is below the firstpredetermined threshold, a search query based on the second score of thefirst type of sentiment attribute corresponding to the citation to thecase cited in the first text file; and process the search query toreturn a second text file from the plurality of text files that includesa citation to the case, wherein the citation to the case included in thesecond text file matches the second score of the first type of sentimentattribute corresponding to the citation to the case cited in the firsttext file.
 7. The computer program product of claim 6, wherein the firstscore and the second score are determined based, at least in part, on alevel of intensity of a sentiment attribute corresponding to a citationto the case.
 8. The computer program product of claim 6, furthercomprising program instructions to flag the citation in the first textfile if the first degree of similarity between the first average scoreof the first type of sentiment attribute associated with the first setof citations to the case cited in the plurality of text files and thesecond score of the first type of sentiment attribute associated withthe citation to the case cited in the first text file is below the firstpredetermined threshold.
 9. The computer program product of claim 6,further comprising program instructions to: aggregate a first portion ofcitations included in the first set of citations, wherein: each citationin the first portion of citations corresponds to at least a firstentity; and each citation in the first portion of citations includes thefirst type of sentiment attribute.
 10. The computer program product ofclaim 9, further comprising program instructions to: determine that asecond degree of similarity between the second score of the first typeof sentiment attribute associated with the citation to the case cited inthe first text file and a second average score of the first type ofsentiment attribute associated with the first portion of citations tothe case cited in the plurality of text files is below a predeterminedthreshold.
 11. A computer system, the computer system comprising: one ormore computer processors; one or more non-transitory computer readablestorage media; computer program instructions; said computer programinstructions being stored on said one or more non-transitory computerreadable storage media for execution by said one or more computerprocessors; and said computer program instructions comprisinginstructions to: determine a first set of sentiment attributescorresponding to a first set of citations to a case cited in a pluralityof text files based, at least in part, on (i) natural languageprocessing and (ii) sentiment analysis; generate a first score for eachtype of sentiment attribute associated with a citation to the case inthe first set of citations; determine a second set of sentimentattributes corresponding to a citation to the case cited in a first textfile that is outside of the plurality of text files based, at least inpart, on (i) natural language processing and (ii) sentiment analysis;generate a second score for each type of sentiment attribute associatedwith the citation to the case in the first text file; determine that afirst degree of similarity between a first average score of a first typeof sentiment attribute associated with the first set of citations to thecase cited in the plurality of text files and the second score of thefirst type of sentiment attribute associated with the citation to thecase cited in the first text file is below a first predeterminedthreshold; generate, in response to the determination that the firstdegree of similarity is below the first predetermined threshold, asearch query based on the second score of the first type of sentimentattribute corresponding to the citation to the case cited in the firsttext file; and process the search query to return a second text filefrom the plurality of text files that includes a citation to the case,wherein the citation to the case included in the second text filematches the second score of the first type of sentiment attributecorresponding to the citation to the case cited in the first text file.12. The computer system of claim 11, wherein the first score and thesecond score are determined based, at least in part, on a level ofintensity of a sentiment attribute corresponding to a citation to thecase.
 13. The computer system of claim 11, further comprising programinstructions to flag the citation in the first text file if the firstdegree of similarity between the first average score of the first typeof sentiment attribute associated with the first set of citations to thecase cited in the plurality of text files and the second score of thefirst type of sentiment attribute associated with the citation to thecase cited in the first text file is below the first predeterminedthreshold.
 14. The computer system of claim 11, further comprisingprogram instructions to: aggregate a first portion of citations includedin the first set of citations, wherein: each citation in the firstportion of citations corresponds to at least a first entity; and eachcitation in the first portion of citations includes the first type ofsentiment attribute.